Risk-Adapted Therapy in Early-Stage Chronic Lymphocytic Leukemia

Chronic lymphocytic leukemia (CLL) is the most common leukemia in adults and usually affects the elderly patient. More than 50% of CLL cases are diagnosed at an early disease stage, often as an incidental lymphocytosis found in a routine blood screen. For about 40 years, the classifications according to Binet or Rai have been the hands-on staging systems to stratify patients in daily clinical practice. An increasing molecular understanding of the disease and the identification of strong prognostic markers, such as genetic lesions in TP53, have urged clinical scientists to create new scoring systems that improve prognostic risk assessment and treatment allocation. Until today, studies on early treatment interventions in asymptomatic patients using single chemo-or combined chemoimmunotherapy have failed to demonstrate a survival benefit. However, improved risk stratification tools integrating molecular disease features and the availability of new targeted drugs with attractive efficacy and limited toxicity might open new possibilities to re-investigate early treatment in well-defined clinical settings in the future. (C) 2016 S. Karger GmbH, Freibur

Semi-automated cancer genome analysis using high-performance computing

Next-generation sequencing (NGS) has turned from a new and experimental technology into a standard procedure for cancer genome studies and clinical investigation. While a multitude of software packages for cancer genome data analysis have been made available, these need to be combined into efficient analytical workflows that cover multiple aspects relevant to a clinical environment and that deliver handy results within a reasonable time frame. Here, we introduce Quick-NGS Cancer as a new suite of bioinformatics pipelines that is focused on cancer genomics and significantly reduces the analytical hurdles that still limit a broader applicability of NGS technology, particularly to clinically driven research. QuickNGS Cancer allows a highly efficient analysis of a broad variety of NGS data types, specifically considering cancer-specific issues, such as biases introduced by tumor impurity and aneuploidy or the assessment of genomic variations regarding their biomedical relevance. It delivers highly reproducible analysis results ready for interpretation within only a few days after sequencing, as shown by a reanalysis of 140 tumor/normal pairs from The Cancer Genome Atlas (TCGA) in which QuickNGS Cancer detected a significant number of mutations calling pipeline. Finally, QuickNGS Cancer obtained several unexpected mutations in leukemias that could be confirmed by Sanger sequencing

Organometallic nucleosides induce non-classical leukemic cell death that is mitochondrial-ROS dependent and facilitated by TCL1-oncogene burden

Background Redox stress is a hallmark of the rewired metabolic phenotype of cancer. The underlying dysregulation of reactive oxygen species (ROS) is interconnected with abnormal mitochondrial biogenesis and function. In chronic lymphocytic leukemia (CLL), elevated ROS are implicated in clonal outgrowth and drug resistance. The pro-survival oncogene T-cell leukemia 1 (TCL1) is causally linked to the high threshold towards classical apoptosis in CLL. We investigated how aberrant redox characteristics and bioenergetics of CLL are impacted by TCL1 and if this is therapeutically exploitable. Methods Bio-organometallic chemistry provided compounds containing a cytosine nucleobase, a metal core (ferrocene, ruthenocene, Fe(CO)3), and a 5’-CH2O-TDS substituent. Four of these metal-containing nucleoside analogues (MCNA) were tested for their efficacy and mode of action in CLL patient samples, gene-targeted cell lines, and murine TCL1-transgenic splenocytes. Results The MCNA showed a marked and selective cytotoxicity towards CLL cells. MCNA activity was equally observed in high-risk disease groups, including those of del11q/del17p cytogenetics and of clinical fludarabine resistance. They overcame protective stromal cell interactions. MCNA-evoked PARP-mediated cell death was non-autophagic and non-necrotic as well as caspase- and P53-independent. This unconventional apoptosis involved early increases of ROS, which proved indispensible based on mitigation of MCNA-triggered death by various scavengers. MCNA exposure reduced mitochondrial respiration (oxygen consumption rate; OCR) and induced a rapid membrane depolarization (∆ΨM). These characteristics distinguished the MCNA from the alkylator bendamustine and from fludarabine. Higher cellular ROS and increased MCNA sensitivity were linked to TCL1 expression. The presence of TCL1 promoted a mitochondrial release of in part caspase-independent apoptotic factors (AIF, Smac, Cytochrome-c) in response to MCNA. Although basal mitochondrial respiration (OCR) and maximal respiratory capacity were not affected by TCL1 overexpression, it mediated a reduced aerobic glycolysis (lactate production) and a higher fraction of oxygen consumption coupled to ATP-synthesis. Conclusions Redox-active substances such as organometallic nucleosides can confer specific cytotoxicity to ROS-stressed cancer cells. Their P53- and caspase-independent induction of non-classical apoptosis implicates that redox-based strategies can overcome resistance to conventional apoptotic triggers. The high TCL1-oncogenic burden of aggressive CLL cells instructs their particular dependence on mitochondrial energetic flux and renders them more susceptible towards agents interfering in mitochondrial homeostasis

Organometallic nucleosides induce non-classical leukemic cell death that is mitochondrial-ROS dependent and facilitated by TCL1-oncogene burden

Background: Redox stress is a hallmark of the rewired metabolic phenotype of cancer. The underlying dysregulation of reactive oxygen species (ROS) is interconnected with abnormal mitochondrial biogenesis and function. In chronic lymphocytic leukemia (CLL), elevated ROS are implicated in clonal outgrowth and drug resistance. The pro-survival oncogene T-cell leukemia 1 (TCL1) is causally linked to the high threshold towards classical apoptosis in CLL. We investigated how aberrant redox characteristics and bioenergetics of CLL are impacted by TCL1 and if this is therapeutically exploitable. Methods: Bio-organometallic chemistry provided compounds containing a cytosine nucleobase, a metal core (ferrocene, ruthenocene, Fe(CO) 3), and a 5'-CH2O-TDS substituent. Four of these metal-containing nucleoside analogues (MCNA) were tested for their efficacy and mode of action in CLL patient samples, gene-targeted cell lines, and murine TCL1-transgenic splenocytes. Results: The MCNA showed a marked and selective cytotoxicity towards CLL cells. MCNA activity was equally observed in high-risk disease groups, including those of del11q/del17p cytogenetics and of clinical fludarabine resistance. They overcame protective stromal cell interactions. MCNA-evoked PARP-mediated cell death was non-autophagic and non-necrotic as well as caspase- and P53-independent. This unconventional apoptosis involved early increases of ROS, which proved indispensible based on mitigation of MCNA-triggered death by various scavengers. MCNA exposure reduced mitochondrial respiration (oxygen consumption rate; OCR) and induced a rapid membrane depolarization (Delta psi M). These characteristics distinguished the MCNA from the alkylator bendamustine and from fludarabine. Higher cellular ROS and increased MCNA sensitivity were linked to TCL1 expression. The presence of TCL1 promoted a mitochondrial release of in part caspase-independent apoptotic factors (AIF, Smac, Cytochrome-c) in response to MCNA. Although basal mitochondrial respiration (OCR) and maximal respiratory capacity were not affected by TCL1 overexpression, it mediated a reduced aerobic glycolysis (lactate production) and a higher fraction of oxygen consumption coupled to ATP-synthesis. Conclusions: Redox-active substances such as organometallic nucleosides can confer specific cytotoxicity to ROS-stressed cancer cells. Their P53- and caspase-independent induction of non-classical apoptosis implicates that redox-based strategies can overcome resistance to conventional apoptotic triggers. The high TCL1-oncogenic burden of aggressive CLL cells instructs their particular dependence on mitochondrial energetic flux and renders them more susceptible towards agents interfering in mitochondrial homeostasis

Inferring clonal heterogeneity in cancer using SNP arrays and whole genome sequencing

Motivation: Clonal heterogeneity is common in many types of cancer, including chronic lymphocytic leukemia (CLL). Previous research suggests that the presence of multiple distinct cancer clones is associated with clinical outcome. Detection of clonal heterogeneity from high throughput data, such as sequencing or single nucleotide polymorphism (SNP) array data, is important for gaining a better understanding of cancer and may improve prediction of clinical outcome or response to treatment. Here, we present a new method, CloneSeeker, for inferring clinical heterogeneity from sequencing data, SNP array data, or both. Results: We generated simulated SNP array and sequencing data and applied CloneSeeker along with two other methods. We demonstrate that CloneSeeker is more accurate than existing algorithms at determining the number of clones, distribution of cancer cells among clones, and mutation and/or copy numbers belonging to each clone. Next, we applied CloneSeeker to SNP array data from samples of 258 previously untreated CLL patients to gain a better understanding of the characteristics of CLL tumors and to elucidate the relationship between clonal heterogeneity and clinical outcome. We found that a significant majority of CLL patients appear to have multiple clones distinguished by copy number alterations alone. We also found that the presence of multiple clones corresponded with significantly worse survival among CLL patients. These findings may prove useful for improving the accuracy of prognosis and design of treatment strategies

Trisomy 12 chronic lymphocytic leukemia expresses a unique set of activated and targetable pathways

Although trisomy 12 (+12) chronic lymphocytic leukemia (CLL) comprises about 20% of cases, relatively little is known about its pathophysiology. These cases often demonstrate atypical morphological and immunophenotypic features, high proliferative rates, unmutated immunoglobulin heavy chain variable region genes, and a high frequency of NOTCH1 mutation. Patients with +12 CLL have an intermediate prognosis, and show higher incidences of thrombocytopenia, Richter transformation, and other secondary cancers. Despite these important differences, relatively few transcriptional profiling studies have focused on identifying dysregulated pathways that characterize +12 CLL, and most have used a hierarchical cytogenetic classification in which cases with more than one recurrent abnormality are categorized according to the abnormality with the poorest prognosis. In this study, we sought to identify protein-coding genes whose expression contributes to the unique pathophysiology of +12 CLL. To exclude the likely confounding effects of multiple cytogenetic abnormalities on gene expression, our +12 patient cohort had +12 as the sole abnormality. We profiled samples obtained from 147 treatment-naive patients. We compared cases with +12 as the only cytogenetic abnormality to cases with only del(13q), del(11q), or diploid cytogenetics using independent discovery (n=97) and validation (n=50) sets. We demonstrate that CLL cases with +12 as the sole abnormality express a unique set of activated pathways compared to other cytogenetic subtypes. Among these pathways, we identify the NFAT signaling pathway and the immune checkpoint molecule, NT5E (CD73), which may represent new therapeutic targets

Altered DNA Methylation Profiles in SF3B1 Mutated CLL Patients

Mutations in splicing factor genes have a severe impact on the survival of cancer patients. Splicing factor 3b subunit 1 (SF3B1) is one of the most frequently mutated genes in chronic lymphocytic leukemia (CLL); patients carrying these mutations have a poor prognosis. Since the splicing machinery and the epigenome are closely interconnected, we investigated whether these alterations may affect the epigenomes of CLL patients. While an overall hypomethylation during CLL carcinogenesis has been observed, the interplay between the epigenetic stage of the originating B cells and SF3B1 mutations, and the subsequent effect of the mutations on methylation alterations in CLL, have not been investigated. We profiled the genome-wide DNA methylation patterns of 27 CLL patients with and without SF3B1 mutations and identified local decreases in methylation levels in SF3B1(mut) CLL patients at 67 genomic regions, mostly in proximity to telomeric regions. These differentially methylated regions (DMRs) were enriched in gene bodies of cancer-related signaling genes, e.g., NOTCH1, HTRA3, and BCL9L. In our study, SF3B1 mutations exclusively emerged in two out of three epigenetic stages of the originating B cells. However, not all the DMRs could be associated with the methylation programming of B cells during development, suggesting that mutations in SF3B1 cause additional epigenetic aberrations during carcinogenesis

Machine learning can identify newly diagnosed patients with CLL at high risk of infection

Infections have become the major cause of morbidity and mortality among patients with chronic lymphocytic leukemia (CLL) due to immune dysfunction and cytotoxic CLL treatment. Yet, predictive models for infection are missing. In this work, we develop the CLL Treatment-Infection Model (CLL-TIM) that identifies patients at risk of infection or CLL treatment within 2 years of diagnosis as validated on both internal and external cohorts. CLL-TIM is an ensemble algorithm composed of 28 machine learning algorithms based on data from 4,149 patients with CLL. The model is capable of dealing with heterogeneous data, including the high rates of missing data to be expected in the real-world setting, with a precision of 72% and a recall of 75%. To address concerns regarding the use of complex machine learning algorithms in the clinic, for each patient with CLL, CLL-TIM provides explainable predictions through uncertainty estimates and personalized risk factors